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AI EngineeringTeX

TorchForge RL Training

by Orchestra-Research

TorchForge RL Training is an AI Engineering skill for Claude Code, published by Orchestra-Research in AI-Research-SKILLs.

11.6K stars838 forkson Orchestra-Research/AI-Research-SKILLsAdded 2026/07/19+1% in starsRepository updated 2026/06/16
View source on GitHubDownload repo ZIP
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Install in seconds
Install TorchForge RL Training
Copy TorchForge RL Training into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/06-post-training/torchforge ~/.claude/skills/torchforge

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/Orchestra-Research/AI-Research-SKILLs.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
06-post-training/torchforge/SKILL.md in Orchestra-Research/AI-Research-SKILLs
Installs to
~/.claude/skills/torchforge
Collection
One of 50 skills cataloged from this repository
Category
AI Engineering — 3670 skills

What TorchForge RL Training does

Provides guidance for implementing agentic reinforcement learning (RL) using Meta's torchforge library. Use when you need clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.

TorchForge RL Training is cataloged under AI Engineering on DirSkills. TorchForge RL Training comes from a repository tagged ai, ai-research, claude, claude-code and claude-skills.

Documentation

README

torchforge: PyTorch-Native Agentic RL Library

torchforge is Meta's PyTorch-native RL library that separates infrastructure concerns from algorithm concerns. It enables rapid RL research by letting you focus on algorithms while handling distributed training, inference, and weight sync automatically.

When to Use torchforge

Choose torchforge when you need:

  • Clean separation between RL algorithms and infrastructure
  • PyTorch-native abstractions (no Ray dependency)
  • Easy algorithm experimentation (GRPO, DAPO, SAPO in ~100 lines)
  • Scalable training with Monarch actor system
  • Integration with TorchTitan for model parallelism

This is the opening of the README. Read the full README on GitHub.

Frequently asked about TorchForge RL Training

  • What else does Orchestra-Research publish alongside TorchForge RL Training?

    TorchForge RL Training is one of 50 skills that DirSkills catalogs from Orchestra-Research/AI-Research-SKILLs, the repository it ships in. Its siblings there include AWQ Quantization, Autoresearch and Axolotl. Each one is a separate skill with its own page in this directory, installs the same way TorchForge RL Training does, and is maintained by Orchestra-Research in that same repository. The rest of the collection is listed on the Orchestra-Research/AI-Research-SKILLs page.

  • How does TorchForge RL Training compare to other AI Engineering skills?

    TorchForge RL Training ranks #366 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of TorchForge RL Training against them. Open each page to compare what they document and how they install.

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